Part-dependent Label Noise: Towards Instance-dependent Label Noise (2006.07836v2)

Abstract: Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evidences showing that we humans perceive instances by decomposing them into parts. Annotators are therefore more likely to annotate instances based on the parts rather than the whole instances, where a wrong mapping from parts to classes may cause the instance-dependent label noise. Motivated by this human cognition, in this paper, we approximate the instance-dependent label noise by exploiting \textit{part-dependent} label noise. Specifically, since instances can be approximately reconstructed by a combination of parts, we approximate the instance-dependent \textit{transition matrix} for an instance by a combination of the transition matrices for the parts of the instance. The transition matrices for parts can be learned by exploiting anchor points (i.e., data points that belong to a specific class almost surely). Empirical evaluations on synthetic and real-world datasets demonstrate our method is superior to the state-of-the-art approaches for learning from the instance-dependent label noise.

• The paper introduces a part-dependent method to approximate instance-dependent noise by leveraging parts-based representations inspired by human cognition.
• It constructs transition matrices using anchor points to model noise at the part level, effectively addressing identifiability issues common in noisy label learning.
• Empirical results on datasets like CIFAR-10 show up to a 10% accuracy improvement under heavy noise, demonstrating the method's robustness and scalability.

An Overview of Part-Dependent Label Noise: Towards Instance-Dependent Label Noise

The paper presents a novel method for addressing the challenges posed by instance-dependent label noise (IDN) in machine learning. Traditional approaches to learning with noisy labels often assume that noise is either random or class-dependent; however, these assumptions do not hold for many real-world scenarios where noise depends on specific instance characteristics. The authors introduce part-dependent label noise as an intermediate and practical approach to approximate the IDN problem, leveraging human cognitive behaviors and part-based representations that have shown promise in psychological and computational theories.

Part-Dependent Noise Approximation

The crux of the paper is the approximation of the instance-dependent transition matrix through part-dependent transition matrices. Recognizing that human annotators often label instances by observing their constituent parts rather than the whole, the authors propose that noise can similarly be modeled at the part level. This assumption is grounded in evidence from cognitive psychology and has been computationally validated through parts-based learning algorithms such as non-negative matrix factorization (NMF). The method theorizes that noise rates can be predicted as weighted combinations of noise rates at the part level, thus allowing for more realistic modeling of label noise.

Learning Transition Matrices

To implement this model, the authors develop a framework for learning part-dependent transition matrices using anchor points—examples thought to belong to a specific class with absolute certainty. Transition matrices at the part level are constructed by learning how these anchor points are misrepresented due to noise, establishing a way to approximate the transition matrices for entire instances. The approach mitigates the identifiability issue of IDN by asserting that the parameters used to reconstruct an instance can be employed to reconstruct its noise characteristics. This approach requires no assumption of instance-independent noise rates, presenting a robust method for tackling ambiguities tied to IDN.

Empirical Evaluation and Results

Empirical evaluations on synthetic and real-world datasets demonstrate that the proposed method outperforms existing state-of-the-art techniques across a range of noise levels, particularly in scenarios with heavy noise contamination. Notably, when noise levels are high, improvements in test accuracy by nearly 10% were observed in the case of CIFAR-10 data. This underscores the strength and applicability of part-dependent transition matrices in practical scenarios, offering compelling evidence for its superiority over existing methods.

Implications and Future Research

The implications for both theoretical exploration and practical application are significant. The paper suggests that examining label noise through the lens of part-based structures aligns well with the cognitive strategies employed by humans and provides a fruitful direction for future research in AI. The approach is scalable and adaptable for various machine learning problems involving noisy labels. Future explorations might include leveraging additional priors or constraints on parts, potentially extending into different problem domains where noisy data is prevalent. Another potential avenue could be the application of slack variables to modify combination parameters of parts-derived transition matrices, further refining the approach.

The insights from this work promise to facilitate more efficient, robust classification models in industrial applications where ample noisy data is available, shifting the reliance from high-cost accurately labeled datasets to those that include label imperfections. The proposed method also opens doors to more fine-grained scrutiny of label noise, pushing theoretical boundaries and fostering advancements in computational methodologies for noisy label learning.